Reducing suffering in animal models and procedures involving seizures, convulsions and epilepsy

This report is based on discussions and submissions from an expert working group consisting of veterinarians, animal care staff and scientists with expert knowledge relevant to the field and aims to facilitate the implementation of the Three Rs (replacement, reduction and refinement) in the use of animal models or procedures involving seizures, convulsions and epilepsy. Each of these conditions will be considered, the specific welfare issues discussed, and practical measures to reduce animal use and suffering suggested. The emphasis is on refinement since this has the greatest potential for immediate implementation, and some general issues for refinement are summarised to help achieve this, with more detail provided on a range of specific refinements.

Psychiatric adverse effects of zonisamide in patients with epilepsy and mental disorder comorbidities

Over the last few years, zonisamide has been proposed as a potentially useful medication for patients with focal seizures, with or without secondary generalization. Since psychiatric adverse effects, including mania, psychosis, and suicidal ideation, have been associated with its use, it was suggested that the presence of antecedent psychiatric disorders is an important factor associated with the discontinuation of zonisamide therapy in patients with epilepsy. We, therefore, set out to assess the tolerability profile of zonisamide in a retrospective chart review of 23 patients with epilepsy and comorbid mental disorders, recruited from two specialist pediatric (n=11) and adult (n=12) neuropsychiatry clinics. All patients had a clinical diagnosis of treatment-refractory epilepsy after extensive neurophysiological and neuroimaging investigations. The vast majority of patients (n=22/23, 95.7%) had tried previous antiepileptic medications, and most adult patients (n=9/11, 81.8%) were on concomitant medication for epilepsy. In the majority of cases, the psychiatric adverse effects of zonisamide were not severe. Four patients (17.4%) discontinued zonisamide because of lack of efficacy, whereas only one patient (4.3%) discontinued it because of the severity of psychiatric adverse effects (major depressive disorder). The low discontinuation rate of zonisamide in a selected population of patients with epilepsy and neuropsychiatric comorbidity suggests that this medication is safe and reasonably well-tolerated for use in patients with treatment-refractory epilepsy. Given the limitations of the present study, including the relatively small sample size, further research is warranted to confirm this finding. © 2013 Elsevier Inc.

Magnetoencephalography in the study of epilepsy and consciousness

The neural bases of altered consciousness in patients with epilepsy during seizures and at rest have raised significant interest in the last decade. This exponential growth has been supported by the parallel development of techniques and methods to investigate brain function noninvasively with unprecedented spatial and temporal resolution. In this article, we review the contribution of magnetoencephalography to deconvolve the bioelectrical changes associated with impaired consciousness during seizures. We use data collected from a patient with refractory absence seizures to discuss how spike-wave discharges are associated with perturbations in optimal connectivity within and between brain regions and discuss indirect evidence to suggest that this phenomenon might explain the cognitive deficits experienced during prolonged 3/s spike-wave discharges. © 2013 Elsevier Inc.

Photosensitive benign myoclonic epilepsy in infancy

Purpose: To describe the electroclinical features of subjects who presented with a photosensitive benign myoclonic epilepsy in infancy (PBMEI). Methods: The patients were selected from a group of epileptic subjects with seizure onset in infancy or early childhood. Inclusion criteria were the presence of photic-induced myoclonic seizures and a favorable outcome. Cases with less than 24 month follow up were excluded from the analysis. Results: Eight patients were identified (4 males, 4 females). Personal history was uneventful. All of them had familial antecedents of epilepsy. Psychomotor development was normal in 6 cases, both before and after seizure onset. One patient showed a mild mental retardation and a further patient showed some behavioral disturbances. Neuroradiological investigations, when performed (5 cases), gave normal results. The clinical manifestations were typical and could vary from upward movements of the eyes to myoclonic jerks of the head and shoulders, isolated or briefly repetitive, never causing a fall. Age of onset was between 11 months and 3 years and 2 months. Characteristically, the seizures were always triggered by photic stimulation. Non photo-induced spontaneous myoclonic attacks were reported in 2 cases during the follow-up. Other types of seizures were present at follow-up in 2 cases. The outcome was favorable, even if, usually, seizure control required high AED plasma levels. Since the clinical symptoms were not recognized early, some patients were treated only many years after the onset of symptoms. Conclusion: Among BMEI patients, our cases constitute a subgroup in which myoclonic jerks were always triggered by photostimulation, in particular at onset of their epilepsy. © 2006 International League Against Epilepsy.

Interictal autonomic abnormalities in idiopathic Rolandic Epilepsy

We investigated 50 young patients with a diagnosis of Rolandic Epilepsy (RE) for the presence of abnormalities in autonomic tone compared with 50 young patients with idiopathic generalized epilepsy with absences and 50 typically developing children of comparable age. We analyzed time domain (N-N interval, pNN50) and frequency domain (High Frequency (HF), Low Frequency (LF) and LF/HF ratio) indices from ten-minute resting EKG activity. Patients with RE showed significantly higher HF and lower LF power and lower LF/HF ratio than controls, independent of the epilepsy group, and did not show significant differences in any other autonomic index with respect to the two control groups. In RE, we found a negative relationship between both seizure load and frequency of sleep interictal EEG abnormalities with parasympathetic drive levels. These changes might be the expression of adaptive mechanisms to prevent the excessive sympathetic drive seen in patients with refractory epilepsies. © 2012 Elsevier Inc.

A low mortality, high morbidity Reduced Intensity Status Epilepticus (RISE) model of epilepsy and epileptogenesis in the rat

Animal models of acquired epilepsies aim to provide researchers with tools for use in understanding the processes underlying the acquisition, development and establishment of the disorder. Typically, following a systemic or local insult, vulnerable brain regions undergo a process leading to the development, over time, of spontaneous recurrent seizures. Many such models make use of a period of intense seizure activity or status epilepticus, and this may be associated with high mortality and/or global damage to large areas of the brain. These undesirable elements have driven improvements in the design of chronic epilepsy models, for example the lithium-pilocarpine epileptogenesis model. Here, we present an optimised model of chronic epilepsy that reduces mortality to 1% whilst retaining features of high epileptogenicity and development of spontaneous seizures. Using local field potential recordings from hippocampus in vitro as a probe, we show that the model does not result in significant loss of neuronal network function in area CA3 and, instead, subtle alterations in network dynamics appear during a process of epileptogenesis, which eventually leads to a chronic seizure state. The model’s features of very low mortality and high morbidity in the absence of global neuronal damage offer the chance to explore the processes underlying epileptogenesis in detail, in a population of animals not defined by their resistance to seizures, whilst acknowledging and being driven by the 3Rs (Replacement, Refinement and Reduction of animal use in scientific procedures) principles.

Clinical characteristics of patients with epilepsy in a specialist neuropsychiatry service

Neuropsychiatry services provide specialist input into the assessment and management of behavioral symptoms associated with a range of neurological conditions, including epilepsy. Despite the centrality of epilepsy to neuropsychiatry and the recent expansion of neuropsychiatry service provision, little is known about the clinical characteristics of patients with epilepsy who are routinely seen by a specialist neuropsychiatry service. This retrospective study filled this gap by retrospectively evaluating a naturalistic series of 60 consecutive patients with epilepsy referred to and assessed within a neuropsychiatry setting. Fifty-two patients (86.7%) had active epilepsy and were under the ongoing care of the referring neurologist for seizure management. The majority of patients (N = 42; 70.0%) had a diagnosis of localization-related epilepsy, with temporal lobe epilepsy as the most common epilepsy type (N = 37; 61.7%). Following clinical assessment, 39 patients (65.0%) fulfilled formal diagnostic criteria for at least one psychiatric disorder; nonepileptic attack disorder (N = 37; 61.7%), major depression (N = 23; 38.3%), and generalized anxiety disorder (N = 16; 26.7%) were the most commonly diagnosed comorbidities. The clinical characteristics of patients seen in specialist neuropsychiatry settings are in line with the results from previous studies in neurology clinics in terms of both epilepsy and psychiatric comorbidity. Our findings confirm the need for the development and implementation of structured care pathways for the neuropsychiatric aspects of epilepsy, with focus on comorbid nonepileptic attacks and affective and anxiety symptoms. This is of particular importance in consideration of the impact of behavioral symptoms on patients' health-related quality of life.

A research platform for artificial neural networks with applications in pediatric epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that (1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and (2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Epilepsy in Bipolar Disorder: Impact on Clinical Features, Course and Outcome

Background and Aims: It is well recognized that mood disorders and epilepsy commonly co-occur. However, the relationship between epilepsy and the clinical features and course of illness in bipolar disorder (BD) is currently unknown. Here we explore the rate of epilepsy within a large sample of individuals with BD and examine bipolar illness characteristics according to the presence or absence of epilepsy. Methods: 1596 participants recruited to the Bipolar Disorder Research Network; a well-defined sample of UK subjects with a diagnosis of BD, completed a self-report questionnaire to assess lifetime history of epilepsy (Ottman et al., 2010). A subset of participants (n = 29) completed a telephone interview assessment to determine expert-confirmed epilepsy status. Lifetime clinical characteristics of illness were compared between BD subjects with and without a history of epilepsy. Results: 127 individuals (8%) screened positively for lifetime history of epilepsy. Bipolar subjects with epilepsy experienced higher rates of: suicide attempt (64.2% vs. 47.4%, p = 0.000367); panic disorder (29.6% vs. 16.1%, p = 0.001); phobias (13.6% vs. 5.7%, 0.004); alcohol abuse (18.6% vs. 10.6%, p = 0.017); and other substance abuse (10.2% vs. 4%, p = 0.009). History of suicide attempt (OR = 1.79, p = 0.013) remained significant within a multivariate model. Similar trends were observed within bipolar subjects with well-defined, expert-confirmed epilepsy (n = 29). Conclusions: Results demonstrate an increased rate of self-reported epilepsy in the BD sample, compared to the general population, and suggest differences in the clinical course of BD according to the presence of epilepsy. Comorbid epilepsy within BD may provide an attractive opportunity for subcategorising for future genetic studies, potentially identifying common underlying mechanisms.

Verbal emotional memory in children and adolescents with temporal lobe epilepsy: A first study

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